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Qualitative Research

Open Coding

Open Coding

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Definition:

Open coding is a foundational qualitative research method drawn from grounded theory, in which analysts systematically work through raw data and assign short descriptive labels to meaningful segments. Unlike deductive coding, open coding is inductive: researchers follow what the data actually says rather than applying a fixed framework in advance. The process surfaces patterns, tensions, and themes that might otherwise go unnoticed, making it especially valuable in exploratory consumer research, brand perception studies, and concept testing. In qualitative research practice, open coding typically precedes axial and selective coding, progressively moving from granular observations toward higher-order themes that can inform strategic decisions.

How Conveo Does It

Conveo applies open coding automatically as AI-moderated video interviews are completed, tagging segments of speech, tone, and behavior with descriptive codes drawn from real participant responses. Studies can launch in under 30 minutes, and coded outputs are available within days across hundreds of concurrent interviews. Because every session involves real participants captured on video, not synthetic respondents, the codes reflect genuine human language, hesitation, and emotion, giving enterprise research teams a traceable foundation for thematic analysis.

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Frequently asked questions.

Open coding is the process of reading or reviewing raw qualitative data and assigning short descriptive labels to meaningful segments. It is inductive by design, meaning researchers let the data guide the labels rather than applying a predetermined framework. The goal is to break down complex responses into manageable units that can later be grouped into patterns and themes during subsequent analysis stages.
Open coding matters because it forces researchers to engage closely with raw data before drawing conclusions. By labeling what participants actually said, rather than what researchers expected to hear, the process reduces confirmation bias and surfaces unexpected findings. For enterprise teams running concept tests, brand studies, or customer satisfaction research, open coding creates an auditable trail from raw participant language to the strategic themes that inform decisions.
Open coding and axial coding are sequential stages in grounded theory analysis. Open coding breaks raw data into discrete labeled segments without imposing structure. Axial coding comes next, grouping those initial codes into categories and mapping the relationships between them. Think of open coding as disaggregating the data and axial coding as reassembling it into a coherent structure. Both stages are necessary before researchers can identify the core themes that drive stakeholder-ready findings.
AI is accelerating open coding by processing large volumes of transcripts and recordings in parallel, assigning descriptive labels far faster than manual analysis allows. Platforms purpose-built for qualitative research can also incorporate tone, facial cues, and behavioral signals alongside spoken words, producing richer initial codes than text-only review. The key distinction is that credible AI coding remains grounded in real participant data, with outputs that researchers can inspect, challenge, and refine rather than accept as a black box.
Enterprise teams typically apply open coding early in the analysis phase, immediately after transcripts or recordings are available. Researchers assign codes independently or in pairs to reduce individual bias, then compare and reconcile labels before moving to thematic grouping. In high-volume studies covering multiple markets or segments, teams often divide the coding workload by participant group. The coded dataset then becomes the foundation for synthesis, stakeholder reporting, and cross-study comparisons stored in a central insight library.

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